Tick-Induced Mammalian Meat Allergy in Australia
National Prevalence and Geographic Distribution from Laboratory Surveillance, 2014-2024
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Preprint Research Notice: The findings and analyses displayed herein are part of ongoing research and are provisional. They may be incomplete, subject to revision, or contain errors.
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Affiliations
1 University of Queensland, Brisbane, Australia
2 CSIRO Health and Biosecurity, Brisbane, Australia
3 QML Pathology, Brisbane, Australia
4 Sullivan Nicolaides Pathology, Brisbane, Australia
5 Douglas Hanley Moir Pathology, Sydney, Australia
6 Laverty Pathology, Sydney, Australia
7 Northern Beaches Hospital, Sydney, Australia
8 National Allergy Centre of Excellence, Australia
9 The University of Sydney, Sydney, Australia
10 TiARA (Tick-induced Allergies Research and Awareness), Australia
* Correspondence to Alexander W. Gofton; alexander.gofton@csiro.au
Published Manuscript
Abstract
Background: Alpha-gal syndrome (AGS), also known as mammalian meat allergy, is an emerging IgE-mediated allergic condition induced by tick bites and characterized by delayed hypersensitivity to galactose-α-1,3-galactose. Despite early Australian reports, comprehensive epidemiological data describing the national burden and distribution of AGS have been lacking.
Methods: We analyzed deidentified α-Gal specific IgE (sIgE) testing data from four major Australian pathology providers spanning January 2014 to December 2024. Data included patient demographics, residential location, test dates, and results. Patients with α-Gal sIgE >0.1 kU/L were classified as suspected AGS cases. We characterized geographic distribution, demographic risk factors, temporal trends, and longitudinal antibody dynamics using spatial statistics, segmented regression, and mixed-effects modeling.
Results: Among 16,562 tests from 14,075 individuals, 5,025 (35.7%) had suspected AGS based on positive serology. Cases were strongly concentrated along Australia’s eastern seaboard within Ixodes holocyclus tick habitat (Gini coefficient 0.86), with marked spatial clustering (Moran’s I = 0.74). Risk increased substantially with age, peaking at 2.91-fold (95% CI: 2.54-3.33) in males aged 65-74 years compared to females aged 25-34 years. Annual testing volume increased 331% (95% CI: 267-395%) from 2014 to 2024, driven by both geographic expansion to new regions and intensified testing within established areas. Among 2,342 patients with serial testing, α-Gal sIgE levels declined at a median rate of 24.3% over follow-up.
Conclusions: This first national epidemiological assessment reveals AGS as a substantial and geographically concentrated health burden in Australia, with age and sex patterns suggesting occupational or recreational outdoor exposure as key risk factors. These baseline data establish the foundation for ongoing surveillance and inform targeted public health interventions in high-burden regions.
About This Interactive Application
This interactive web application provides access to the data visualizations in the Tick-Induced Mammalian Meat Allergy in Australia: National Prevalence and Geographic Distribution from Laboratory Surveillance, 2014-2024 (PUBLICATION DETAILS).
Users can explore:
- Interactive Map (Figuer 6): Explore geographic distribution of suspected MMA cases across Australia
- Figures 1-7: Interactive versions of all manuscript figures with zoom and exploration capabilities
- Data tables and detailed regional statistics
Navigate through the sections below to explore different aspects of the analysis. All figures are interactive and can be zoomed and panned for detailed examination.
Data Sources
Deidentified data were obtained from all a-Gal sIgE immunoCAP™ tests submitted to the following pathology service providers between January 1 2014, and December 31 2024:
These laboratories provide services across all Australian states and territories and conduct the majority of a‑Gal sIgE testing in Australia, although the exact proportion of total testing conducted by these providers is unknown. Data contained a unique deidentified patient, a unique test identifier, patient year of birth, sex, and residential postcode, date of test, and the test result in kilounits of a-Gal sIgE (kU/L). Clinical records, travel histories, and other information were not obtained.
Patients’ residential locations were aggregated to Australian Bureau of Statistics (ABS) statistical areas level 3 (SA3) define by the Australian Statistical Geography Standard Edition 3, which is designed to create meaningful functional areas of regional towns and cities with populations between 30,000 and 130,000.
Adjusted number of suspected MMA cases are displayed as cases per 1M population per year (1M PPY) which adjusts for both regional population size and samples time from (2014-2024) using annual regional population estimates from the ABS as population denominators.
Suspected cases per 1M PPY = (cumulative_incidence_2014-2024 / cumulative_annual_population_2014-2024) * 1000000© 2025 | For questions or feedback, please contact: alexander.gofton@csiro.au
Interactive Map
Figure 6 (interactive version) Geographic distribution of suspected mammalian meat allergy cases per 1 million population per year (1M PPY) in Australia 2014-2024. Dashed red line indicated the approximate western edge of the distribution of the causative tick Ixodes holocyclus.
Data Table
Figure 1. Age and Sex Distribution
Figure 1 Age distribution by sex of all tested people (A) and people with suspected MMA (B).
Figure 1 Age distribution by sex of all tested people (A) and people with suspected MMA (B).
Figure 2. Risk Ratios
Figure 2 Risk ratios comparing: (A) males to females within each age category, (B) each age group to the reference (25–34 years, pooled sexes), and (C) all age-sex combinations to females aged 25–34 years (the lowest-risk group, 18.5% positivity). Filled circles indicate statistical significance (p < 0.01); open circles indicate non-significant comparisons. Error bars represent 95% confidence intervals. Dashed vertical line indicates RR = 1.0 (no difference from reference group).
Figure 2 Risk ratios comparing: (A) males to females within each age category, (B) each age group to the reference (25–34 years, pooled sexes), and (C) all age-sex combinations to females aged 25–34 years (the lowest-risk group, 18.5% positivity). Filled circles indicate statistical significance (p < 0.01); open circles indicate non-significant comparisons. Error bars represent 95% confidence intervals. Dashed vertical line indicates RR = 1.0 (no difference from reference group).
Figure 2 Risk ratios comparing: (A) males to females within each age category, (B) each age group to the reference (25–34 years, pooled sexes), and (C) all age-sex combinations to females aged 25–34 years (the lowest-risk group, 18.5% positivity). Filled circles indicate statistical significance (p < 0.01); open circles indicate non-significant comparisons. Error bars represent 95% confidence intervals. Dashed vertical line indicates RR = 1.0 (no difference from reference group).
Figure 3. Annual Testing Trends
Figure 3 Annual trends in α-Gal sIgE testing in Australia, 2014-2024. (A) Total tests performed. (B) Number of suspected MMA cases. (C) Testing positivity rate. Linear segmented regression models and SE are indicated by red dashed lines and light red areas, and grey dot-dashed line indicated the structural breakpoints identified by the linear segmented regression analysis.
Figure 3 Annual trends in α-Gal sIgE testing in Australia, 2014-2024. (A) Total tests performed. (B) Number of suspected MMA cases. (C) Testing positivity rate. Linear segmented regression models and SE are indicated by red dashed lines and light red areas, and grey dot-dashed line indicated the structural breakpoints identified by the linear segmented regression analysis.
Figure 3 Annual trends in α-Gal sIgE testing in Australia, 2014-2024. (A) Total tests performed. (B) Number of suspected MMA cases. (C) Testing positivity rate. Linear segmented regression models and SE are indicated by red dashed lines and light red areas, and grey dot-dashed line indicated the structural breakpoints identified by the linear segmented regression analysis.
Figure 4. Testing Expansion
Figure 4 Dual expansion of α-Gal sIgE testing infrastructure in Australia, 2014-2024. (A) Geographic expansion: number of SA3 regions conducting testing; shaded area shows CI. (B) Testing intensity: mean tests per region; error bars show standard error. (C) Relative contributions of geographic expansion and testing intensity to overall testing volume growth. (D) Heterogeneity in regional testing intensity changes; violin plot shows probability density, boxplot indicates median (66.7%) and interquartile range (0-200%), and individual points represent SA3 regions. (E) Heatmap showing annual testing volume for the 50 SA3 regions with highest total test numbers from 2014-2024 in descending order.
Figure 4 Dual expansion of α-Gal sIgE testing infrastructure in Australia, 2014-2024. (A) Geographic expansion: number of SA3 regions conducting testing; shaded area shows CI. (B) Testing intensity: mean tests per region; error bars show standard error. (C) Relative contributions of geographic expansion and testing intensity to overall testing volume growth. (D) Heterogeneity in regional testing intensity changes; violin plot shows probability density, boxplot indicates median (66.7%) and interquartile range (0-200%), and individual points represent SA3 regions. (E) Heatmap showing annual testing volume for the 50 SA3 regions with highest total test numbers from 2014-2024 in descending order.
Figure 4 Dual expansion of α-Gal sIgE testing infrastructure in Australia, 2014-2024. (A) Geographic expansion: number of SA3 regions conducting testing; shaded area shows CI. (B) Testing intensity: mean tests per region; error bars show standard error. (C) Relative contributions of geographic expansion and testing intensity to overall testing volume growth. (D) Heterogeneity in regional testing intensity changes; violin plot shows probability density, boxplot indicates median (66.7%) and interquartile range (0-200%), and individual points represent SA3 regions. (E) Heatmap showing annual testing volume for the 50 SA3 regions with highest total test numbers from 2014-2024 in descending order.
Figure 4 Dual expansion of α-Gal sIgE testing infrastructure in Australia, 2014-2024. (A) Geographic expansion: number of SA3 regions conducting testing; shaded area shows CI. (B) Testing intensity: mean tests per region; error bars show standard error. (C) Relative contributions of geographic expansion and testing intensity to overall testing volume growth. (D) Heterogeneity in regional testing intensity changes; violin plot shows probability density, boxplot indicates median (66.7%) and interquartile range (0-200%), and individual points represent SA3 regions. (E) Heatmap showing annual testing volume for the 50 SA3 regions with highest total test numbers from 2014-2024 in descending order.
Figure 4 Dual expansion of α-Gal sIgE testing infrastructure in Australia, 2014-2024. (A) Geographic expansion: number of SA3 regions conducting testing; shaded area shows CI. (B) Testing intensity: mean tests per region; error bars show standard error. (C) Relative contributions of geographic expansion and testing intensity to overall testing volume growth. (D) Heterogeneity in regional testing intensity changes; violin plot shows probability density, boxplot indicates median (66.7%) and interquartile range (0-200%), and individual points represent SA3 regions. (E) Heatmap showing annual testing volume for the 50 SA3 regions with highest total test numbers from 2014-2024 in descending order.
Figure 6. Geographic concentration
Figure 5 Geographic concentration of suspected alpha-gal syndrome cases across Australian SA3 regions. (A) Lorenz curve showing cumulative distribution of cases across SA3 regions (ranked by case burden). The diagonal dashed line represents perfect equality; deviation from this line indicates concentration. (B) Pareto chart showing the proportion of total cases accounted for by cumulative percentages of SA3 regions.
Figure 5 Geographic concentration of suspected alpha-gal syndrome cases across Australian SA3 regions. (A) Lorenz curve showing cumulative distribution of cases across SA3 regions (ranked by case burden). The diagonal dashed line represents perfect equality; deviation from this line indicates concentration. (B) Pareto chart showing the proportion of total cases accounted for by cumulative percentages of SA3 regions.
Figure 6. Maps
See Interactive Map
Figure 7. Alpha-Gal sIgE levels reduce over time
Figure 7 Longitudinal changes in α-Gal sIgE levels in people who had > 2 tests. (A) Distribution of individual people’s annual rate of change (beta) of α-Gal sIgE/year from mixed-effects models. Negative values indicate declining antibody levels over time. Dotted line indicates no change; dashed line indicates the median rate of change across the cohort (-0.28 log α-Gal sIgE kU/L). (B) Distribution of individual people’s percentage change in α-Gal sIgE over time compared to their first test. Dotted line indicates no change; dashed line indicates the median percentage change across the cohort (-24.3%). (C) Patients’ first test α-Gal sIgE levels (log-transformed) vs. their individual annual rate of change. Blue points (rate of decline = < 0) indicates patients whose α-gal sIgE levels decreased over time; teal points above indicate increases. The red line shows the linear regression fit with 95% confidence interval (shaded area). The vertical dotted line indicates the seropositivity threshold (0.1 kU/L, and the horizontal dashed line indicates no change (slope = 0). (D) Scatter plot comparing first and last α-gal sIgE measurements (log-transformed) for each person. Blue points below the diagonal long-dashed line (slope = 1) indicates patients whose α-gal sIgE levels decreased between first and last test; teal points above indicate increases. The red line shows linear regression fit with 95% confidence interval. The positive threshold of 0.1 kU/L alpha-gal sIgE is marked with dotted lines on both axes.
Figure 7 Longitudinal changes in α-Gal sIgE levels in people who had > 2 tests. (A) Distribution of individual people’s annual rate of change (beta) of α-Gal sIgE/year from mixed-effects models. Negative values indicate declining antibody levels over time. Dotted line indicates no change; dashed line indicates the median rate of change across the cohort (-0.28 log α-Gal sIgE kU/L). (B) Distribution of individual people’s percentage change in α-Gal sIgE over time compared to their first test. Dotted line indicates no change; dashed line indicates the median percentage change across the cohort (-24.3%). (C) Patients’ first test α-Gal sIgE levels (log-transformed) vs. their individual annual rate of change. Blue points (rate of decline = < 0) indicates patients whose α-gal sIgE levels decreased over time; teal points above indicate increases. The red line shows the linear regression fit with 95% confidence interval (shaded area). The vertical dotted line indicates the seropositivity threshold (0.1 kU/L, and the horizontal dashed line indicates no change (slope = 0). (D) Scatter plot comparing first and last α-gal sIgE measurements (log-transformed) for each person. Blue points below the diagonal long-dashed line (slope = 1) indicates patients whose α-gal sIgE levels decreased between first and last test; teal points above indicate increases. The red line shows linear regression fit with 95% confidence interval. The positive threshold of 0.1 kU/L alpha-gal sIgE is marked with dotted lines on both axes.
Figure 7 Longitudinal changes in α-Gal sIgE levels in people who had > 2 tests. (A) Distribution of individual people’s annual rate of change (beta) of α-Gal sIgE/year from mixed-effects models. Negative values indicate declining antibody levels over time. Dotted line indicates no change; dashed line indicates the median rate of change across the cohort (-0.28 log α-Gal sIgE kU/L). (B) Distribution of individual people’s percentage change in α-Gal sIgE over time compared to their first test. Dotted line indicates no change; dashed line indicates the median percentage change across the cohort (-24.3%). (C) Patients’ first test α-Gal sIgE levels (log-transformed) vs. their individual annual rate of change. Blue points (rate of decline = < 0) indicates patients whose α-gal sIgE levels decreased over time; teal points above indicate increases. The red line shows the linear regression fit with 95% confidence interval (shaded area). The vertical dotted line indicates the seropositivity threshold (0.1 kU/L, and the horizontal dashed line indicates no change (slope = 0). (D) Scatter plot comparing first and last α-gal sIgE measurements (log-transformed) for each person. Blue points below the diagonal long-dashed line (slope = 1) indicates patients whose α-gal sIgE levels decreased between first and last test; teal points above indicate increases. The red line shows linear regression fit with 95% confidence interval. The positive threshold of 0.1 kU/L alpha-gal sIgE is marked with dotted lines on both axes.
Figure 7 Longitudinal changes in α-Gal sIgE levels in people who had > 2 tests. (A) Distribution of individual people’s annual rate of change (beta) of α-Gal sIgE/year from mixed-effects models. Negative values indicate declining antibody levels over time. Dotted line indicates no change; dashed line indicates the median rate of change across the cohort (-0.28 log α-Gal sIgE kU/L). (B) Distribution of individual people’s percentage change in α-Gal sIgE over time compared to their first test. Dotted line indicates no change; dashed line indicates the median percentage change across the cohort (-24.3%). (C) Patients’ first test α-Gal sIgE levels (log-transformed) vs. their individual annual rate of change. Blue points (rate of decline = < 0) indicates patients whose α-gal sIgE levels decreased over time; teal points above indicate increases. The red line shows the linear regression fit with 95% confidence interval (shaded area). The vertical dotted line indicates the seropositivity threshold (0.1 kU/L, and the horizontal dashed line indicates no change (slope = 0). (D) Scatter plot comparing first and last α-gal sIgE measurements (log-transformed) for each person. Blue points below the diagonal long-dashed line (slope = 1) indicates patients whose α-gal sIgE levels decreased between first and last test; teal points above indicate increases. The red line shows linear regression fit with 95% confidence interval. The positive threshold of 0.1 kU/L alpha-gal sIgE is marked with dotted lines on both axes.